HW5 - Make Data Move
Jakob Grøhn Damgaard
05/10/2020
Explore the recent global developments with R
Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
Get the necessary packages
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
## ── Attaching packages ────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ───────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Look at the data
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
unique(gapminder$year)## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007head(gapminder)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
# Set theme to white background for better visibility
theme_set(theme_bw())
# Plotting gdp per capita with life expectancy for all countries in 1952
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() 
We see an interesting spread with an outlier to the right. Answer the following questions, please:
Q1. Why does it make sense to have a log10 scale on x axis?
Log-scaling the ‘GDP per capita’ data reduces the value range of the variable and, thus, pulls high and low outliers towards the other data points. This scaling aids the visualisation of the data and renders the graph easier to interpret. In the case of our specific data, one high outlier significantly skews the visualisation if scaling is not performed.
Q2. What country is the richest in 1952 (far right on x axis)?
# Findig coutnry with highest gdp per cap in 1952
subset(gapminder, year == 1952) %>% top_n(1, gdpPercap)## # A tibble: 1 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.Running the code above shows that Kuwait is the richest country in 1952.
You can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # Convert x to log scale
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Q3. Can you differentiate the continents by color and fix the axis labels?
ggplot(subset(gapminder, year == 2007),
aes(gdpPercap, lifeExp, size = pop, colour = continent)) +
geom_point() +
scale_fill_brewer("set2")+ # Setting prettier color palette
scale_x_log10()+ # Convert x to log scale
labs(x = "GDP per capita", # Defining all labels
y = "Life expectancy",
colour = "Continent",
size = "Population",
title = "2007")
In my liking, labels are easiest to configure using the ‘labs’ function
Q4. What are the five richest countries in the world in 2007?
# Finding the five richest
subset(gapminder, year == 2007) %>% top_n(5, gdpPercap) ## # A tibble: 5 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Ireland Europe 2007 78.9 4109086 40676.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Norway Europe 2007 80.2 4627926 49357.
## 4 Singapore Asia 2007 80.0 4553009 47143.
## 5 United States Americas 2007 78.2 301139947 42952.The five richest counties in 2007 are:
1. Norway
2. Kuwait
3. Singapore
4. USA
5. Ireland
Make it move!
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. And there are two ways of animating the gapminder ggplot.
Option 1: Animate using transition_states()
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = continent)) +
geom_point() +
scale_fill_brewer("set2")+ # Setting prettier color palette
scale_x_log10()+ # Convert x to log scale
labs(x = "GDP per capita", # Defining all labels
y = "Life expectancy",
colour = "Continent",
size = "Population")
anim
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the viz inside an html file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
Option 2 Animate using transition_time()
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = continent)) +
geom_point() +
scale_fill_brewer("set2")+ # Setting prettier color palette
scale_x_log10()+ # Convert x to log scale
labs(x = "GDP per capita", # Defining all labels
y = "Life expectancy",
colour = "Continent",
size = "Population") +
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Q5 Can you add a title to one or both of the animations above that will change in sync with the animation? [hint: search labeling for transition_states() and transition_time() functions respectively]
# Add transitioning year title to first animation
anim3 <- anim + transition_states(year, transition_length = 1, state_length = 1) + labs(title = "Year: {closest_state}")
# Show animation 3
anim3 
# Add transitioning year title to second animation
anim4 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = continent)) +
geom_point() +
scale_fill_brewer("set2")+ # Setting prettier color palette
scale_x_log10()+ # Convert x to log scale
labs(x = "GDP per capita", # Defining all labels
y = "Life expectancy",
colour = "Continent",
size = "Population",
title = "Year: {frame_time}") +
transition_time(year)
# Show animation 4
anim4 